CN116933663B - Modeling method and device for aerodynamic characteristics high-fidelity model of airborne turbine rotor - Google Patents
Modeling method and device for aerodynamic characteristics high-fidelity model of airborne turbine rotor Download PDFInfo
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Abstract
The application relates to a modeling method and a modeling device for an airborne turbine rotor aerodynamic characteristic high-fidelity model, wherein the modeling method comprises the following steps: determining wind speed values, blade angle values and rotating speed values corresponding to a plurality of test working condition points according to the value ranges of wind speed, blade angle and rotating speed of the turbine rotor, determining test power values corresponding to each test working condition point, determining optimal values of a plurality of undetermined coefficients in a pre-built turbine rotor mechanism mathematical model according to the wind speed values, the blade angle values, the rotating speed values and the test power values corresponding to all the test working condition points by using a preset optimal solution algorithm, obtaining a turbine rotor simulation model, and training by using a preset machine learning algorithm according to obtained training data to obtain an on-board turbine rotor aerodynamic characteristic high-fidelity model. The method solves the problem that the data is insufficient due to the limitation of test conditions, so that the fit between the generalized model and the personalized product is poor, and the obtained high-fidelity model of the onboard turbine rotor realizes the high-fidelity prediction of the high-wind-speed working condition.
Description
Technical Field
The application relates to the technical field of aircrafts, in particular to a modeling method and device for an airborne turbine rotor aerodynamic characteristic high-fidelity model.
Background
The existing part of the airborne fuel system takes the turbine pump as a power source, the turbine pump mainly transmits fuel pressure to the part of the airborne fuel system, and the output pressure can be regulated in real time according to a system instruction so as to meet the requirements of the part of the airborne fuel system on the fuel pressure and the flow in the full refueling envelope range. The turbine pump can be functionally divided into a turbine rotor assembly and a refuelling pump assembly, and the front turbine rotor is driven to rotate at high speed by utilizing high-speed airflow when the aircraft flies, the rear refuelling pump is driven to boost fuel, and the pitch adjustment rotating speed is changed by controlling the motor so as to ensure that fuel meeting the specified pressure and flow is output in the refuelling process.
The turbine rotor component is difficult to describe the aerodynamic characteristics of products with different configurations through a unified mathematical formula due to different blade configurations, and meanwhile, the running state parameters of the turbine pump cannot be accurately and comprehensively monitored due to the limitation of the types and the number of sensors in an onboard environment, so that the realization of the running state monitoring and predicting functions of part of the onboard fuel system based on the digital twin system is restricted.
Disclosure of Invention
In view of the above, one of the technical problems to be solved by the embodiments of the present application is to provide a modeling method and apparatus for a high-fidelity model of aerodynamic characteristics of an onboard turbine rotor, which are used for overcoming the problems that in the prior art, the aerodynamic characteristics of products with different configurations are difficult to describe through a unified mathematical formula, and the operation state parameters of a turbine pump cannot be accurately and comprehensively monitored.
The embodiment of the application discloses a modeling method of an airborne turbine rotor aerodynamic characteristic high-fidelity model, which comprises the following steps:
according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor, determining wind speed values, blade angle values and rotating speed values corresponding to a plurality of test working condition points;
according to the wind speed value, the blade angle value and the rotating speed value corresponding to each test working point, performing a turbine rotor aerodynamic characteristic test to obtain a test power value corresponding to each test working point;
according to the wind speed value, the blade angle value, the rotating speed value and the test power value corresponding to all the test working condition points, determining the optimal values of a plurality of undetermined coefficients in a pre-constructed turbine rotor mechanism mathematical model by using a preset optimal solution algorithm, and obtaining a turbine rotor simulation model; the turbine rotor mechanism mathematical model is used for calculating and obtaining a mechanism power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the turbine rotor simulation model is used for calculating and obtaining a simulation power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the optimization target of the optimal solution algorithm is that the root mean square error of the test power value and the simulation power value is minimum, and the constraint condition of the preset optimal solution algorithm is the value range of a plurality of undetermined coefficients;
Obtaining training data by using the turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor; the training data are used for representing the corresponding relation between a plurality of simulation power values and wind speed values, blade angle values and rotating speed values;
according to the training data, training by using a preset machine learning algorithm to obtain a high-fidelity model of aerodynamic characteristics of the turbine rotor on board; the airborne turbine rotor aerodynamic characteristic high-fidelity model is used for calculating and obtaining a predicted power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor.
The second aspect of the embodiment of the application discloses a modeling device for an aerodynamic characteristic high-fidelity model of an airborne turbine rotor, which comprises the following components:
the information value determining module is used for determining wind speed values, blade angle values and rotating speed values corresponding to a plurality of test working condition points according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor;
the test power value determining module is used for carrying out a turbine rotor aerodynamic characteristic test according to the wind speed value, the blade angle value and the rotating speed value corresponding to each test working point to obtain a test power value corresponding to each test working point;
the comprehensive obtaining module is used for determining optimal values of a plurality of undetermined coefficients in a pre-built turbine rotor mechanism mathematical model by utilizing a preset optimal solution algorithm according to wind speed values, blade angle values, rotating speed values and test power values corresponding to all test working condition points, and obtaining a turbine rotor simulation model; the turbine rotor mechanism mathematical model is used for calculating and obtaining a mechanism power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the turbine rotor simulation model is used for calculating and obtaining a simulation power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the optimization target of the optimal solution algorithm is that the root mean square error of the test power value and the simulation power value is minimum, and the constraint condition of the preset optimal solution algorithm is the value range of a plurality of undetermined coefficients;
The data acquisition module is used for acquiring training data by utilizing the turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor; the training data are used for representing the corresponding relation between a plurality of simulation power values and wind speed values, blade angle values and rotating speed values;
the high-fidelity model obtaining module is used for obtaining the aerodynamic characteristic high-fidelity model of the turbine rotor of the machine by training through a preset machine learning algorithm according to the training data; the airborne turbine rotor aerodynamic characteristic high-fidelity model is used for calculating and obtaining a predicted power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor.
Compared with the prior art, the method and the device for achieving the aerodynamic performance of the airborne turbine rotor have the advantages that according to the wind speed value, the rotating speed value and the blade angle value, the undetermined coefficient in the turbine rotor mechanism mathematical model is solved by means of a preset optimal solution algorithm to obtain the optimal value of the undetermined coefficient, the turbine rotor simulation model is obtained according to the optimal value of the undetermined coefficient and the turbine rotor mechanism mathematical model, and according to the range of the values of the wind speed, the blade angle and the rotating speed of the turbine rotor and the turbine rotor simulation model, the airborne turbine rotor aerodynamic performance high-fidelity model is obtained by training through preset machine learning. On the basis of a general mathematical mechanism model, the model is trained by using actual low-speed test data of a product in combination with an artificial intelligent algorithm to generate a high-fidelity model, the problem that the data is insufficient due to test condition limitation and the fit of the general model and a personalized product is poor is solved, and the generated high-fidelity model of aerodynamic characteristics of the onboard turbine rotor can realize high-fidelity prediction of the high-wind-speed working condition of the turbine rotor and can accurately and comprehensively monitor the running state parameters of the turbine pump.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of an on-board turbine rotor aerodynamic feature high-fidelity model modeling method in accordance with an example of the present application;
FIG. 2 is a schematic flow chart of a modeling method for an aerodynamic performance high-fidelity model of an onboard turbine rotor disclosed in example II of the present application;
fig. 3 is a schematic structural diagram of an on-board turbine rotor aerodynamic feature high-fidelity modeling apparatus according to an example three of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," "third," and "fourth," etc. in the description and claims of the present application are used for distinguishing between different objects and not for describing a particular sequential order. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or device.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a modeling method of an airborne turbine rotor aerodynamic feature high-fidelity model according to an embodiment of the application, the method includes:
step S101, determining wind speed values, blade angle values and rotating speed values corresponding to a plurality of test working condition points according to the wind speed, blade angle and rotating speed value ranges of the turbine rotor.
In this embodiment, the determination manners of the value ranges of the wind speed, the blade angle and the rotation speed of the turbine rotor are not limited, and can be reasonably selected according to actual application requirements. For example, the measurement data may be obtained through theoretical calculation, may be obtained according to actual measurement data, or may be determined according to expert experience.
Optionally, in order to ensure accuracy of data and considering limitation of ground wind tunnel test conditions, aerodynamic characteristics of the on-board turbine rotor in a low wind speed environment may be preferably tested, and parameters such as wind speed (m/s) of the turbine rotor, blade angle (in degrees) of the turbine rotor, rotational speed (r/min) of the turbine rotor, and output power (in kW) of the turbine rotor are measured and sensed to obtain actual measurement data of aerodynamic characteristics of the turbine rotor. Wherein, the low wind speed environment means that the ambient wind speed is less than or equal to 80m/s, i.e. the maximum test wind speed is less than or equal to 80m/s.
In this embodiment, the range of values of the wind speed, the rotation speed and the blade angle of the turbine rotor is not limited, and may be reasonably selected according to practical application requirements, for example, the range of values of the rotation speed of the turbine rotor may be determined according to the theoretical wind speed of the turbine rotor, the range of values of the rotation speed of the turbine rotor may be determined according to the theoretical rotation speed of the turbine rotor, and the range of values of the blade angle of the turbine rotor may be determined according to the theoretical blade angle of the turbine rotor; the value ranges of the wind speed, the rotating speed and the blade angle of the turbine rotor can be set according to experience; the range of values of the wind speed, the rotational speed and the blade angle of the turbine rotor can also be determined according to the actual measurement data.
Optionally, in order to make the result obtained by solving according to the value ranges of the wind speed, the rotation speed and the blade angle of the turbine rotor more reliable, it may be preferable that the value ranges of the wind speed of the turbine rotor are:
；
the range of the rotating speed of the turbine rotor is，
The range of the blade angle of the turbine rotor is。
For example, the wind speed may be in the range of [40m/s,80m/s ], the rotational speed may be in the range of [0r/min,7000r/min ], and the blade angle may be in the range of [0 °,90 ° ].
In this embodiment, a plurality of test operating points are determined according to a value range of a wind speed of the turbine rotor, a value range of a rotational speed of the turbine rotor, and a value range of a blade angle of the turbine rotor, each test operating point determines a corresponding set of a wind speed value, a rotational speed value, and a blade angle value, and the wind speed value, the blade angle value, and the rotational speed value corresponding to the plurality of test operating points are a plurality of sets of the wind speed value, the blade angle value, and the rotational speed value corresponding to the plurality of test operating points.
And step S102, performing a turbine rotor aerodynamic characteristic test according to the wind speed value, the blade angle value and the rotating speed value corresponding to each test working point to obtain a test power value corresponding to each test working point.
In this embodiment, the set of wind speed values, blade angle values and rotation speed values corresponding to each working condition point obtained in step S101 are respectively subjected to a turbine rotor aerodynamic characteristic test to obtain corresponding power values, and the test power value corresponding to each test working condition point is the power value obtained by performing the aerodynamic characteristic test on the wind speed values, blade angle values and rotation speed values corresponding to each test working condition point.
And step S103, determining optimal values of a plurality of undetermined coefficients in a pre-built turbine rotor mechanism mathematical model by using a preset optimal solution algorithm according to wind speed values, blade angle values, rotating speed values and test power values corresponding to all test working condition points, and obtaining a turbine rotor simulation model.
In this embodiment, the wind speed value, the blade angle value, and the rotation speed value corresponding to all the operating points are the wind speed value, the blade angle value, and the rotation speed value corresponding to the multiple groups of test operating points in the step S101, and the test operating point value corresponding to all the operating points is the power value obtained by performing the aerodynamic characteristic test on the wind speed value, the blade angle value, and the rotation speed value corresponding to the multiple groups of test operating points in the step S102.
In this embodiment, the turbine rotor mechanism mathematical model is used to calculate and obtain a mechanism power value according to the wind speed, the blade angle and the rotation speed of the turbine rotor.
In this embodiment, the turbine rotor simulation model is used to calculate and obtain a simulation power value according to the wind speed, the blade angle and the rotation speed of the turbine rotor.
In the embodiment, the mechanism mathematical model of the turbine rotor is provided、/>、/>、/>、/>、/>As the undetermined coefficient, the undetermined coefficient can float up and down according to the reference value given by the mechanism mathematical model to obtain the value range of the undetermined coefficient, and the value range of the undetermined coefficient can be enlarged or reduced according to the reference value given by the mechanism mathematical model and combined with the actual requirement.
Optionally, in order to make the optimal value of the undetermined coefficient obtained by screening according to the value range of the undetermined coefficient more reliable and more universal, it is preferable thatThe value ranges of (2) are +.>The value range of (2) is +.>、/>The value range of (2) is +.>、/>The value range of (2) is +.>、/>The value range of (2) is +.>、/>The range of the values is as follows、/>The value range of (2) is +.>。
In this embodiment, the value range of the undetermined coefficient is a constraint condition of a preset optimal solution algorithm.
In this embodiment, the type of the optimization target for obtaining the optimal values of the plurality of undetermined coefficients in the turbine rotor mechanism mathematical model by solving the particle swarm algorithm is not limited, and the optimization target can be reasonably set according to practical application requirements, for example, the error of the test power value and the simulation power value is the smallest, the square root error of the test power value and the simulation power value is the smallest, and the root mean square error of the test power value and the simulation power value is the smallest.
Optionally, in order to make the optimal values of a plurality of undetermined coefficients in the turbine rotor mechanism mathematical model obtained by solving more accurate, the optimal values of the undetermined coefficients are prevented from being influenced by deviation of the test power values in the test process, and the optimization targets can be preferably selected as follows: the root mean square error of the test power value and the simulation power value is the smallest.
In this embodiment, the preset optimal solution algorithm is used to obtain the optimal value of the undetermined coefficient by performing the optimizing operation from the value range of the undetermined coefficient, and the type of the preset optimal solution algorithm is not limited, and may be reasonably selected according to the actual application requirement, for example, may be a particle swarm algorithm, may be an ant swarm algorithm, and may also be a dragonfly algorithm.
Optionally, in order to make the optimal value of the undetermined coefficient obtained by screening according to the value range of the undetermined coefficient more accurate and reliable, the optimal solution algorithm may be preset to be a particle swarm algorithm.
Optionally, taking a preset optimal solution algorithm as an example of a particle swarm algorithm, a process of determining optimal values of a plurality of undetermined coefficients in a pre-constructed turbine rotor mechanism mathematical model by the preset optimal solution algorithm is described.
The solving based on the particle swarm algorithm to obtain the optimal value of a plurality of undetermined coefficients in the turbine rotor mechanism mathematical model comprises the following steps:
initializing parameters, including initializing population scale, learning factors, inertia weights, maximum iteration times, a value range of undetermined coefficients, motion speed and the like;
continuously updating the parameter values of the undetermined coefficients to continuously obtain simulation power values so as to meet the optimization target, and comparing the individual optimal values and the global optimal values of the undetermined coefficients obtained by updating;
The round of algorithm is ended by continuously updating until the iteration number reaches the maximum value, and the global optimal solution at the moment is output;
and analyzing whether the optimization target meets the requirement at the moment, if not, repeating the steps by adjusting the initialization parameters such as population scale, inertia weight, maximum iteration number and the like until the value of the coefficient to be determined meeting the optimization target is obtained.
In this embodiment, the optimal values of a plurality of undetermined coefficients in the turbine rotor mechanism mathematical model are a set of undetermined coefficients corresponding to a global optimal solution obtained by solving a particle swarm algorithm.
In this embodiment, the optimal values of the plurality of undetermined coefficients in the turbine rotor mechanism mathematical model are values corresponding to undetermined coefficients which are obtained by solving a preset optimal solution algorithm and meet an optimization target.
In this embodiment, the type of mathematical empirical formula used for constructing the turbine rotor mechanism mathematical model is not limited, and the mathematical empirical formula can be reasonably selected according to practical application requirements, for example, the mathematical empirical formula can be selected according to the rotational speed of the turbine rotor, the mathematical empirical formula can be selected according to the blade angle of the turbine rotor, and the mathematical empirical formula can be selected by comprehensively considering the parameters of the turbine rotor.
In this embodiment, the mode of constructing the turbine rotor mechanism mathematical model is not limited, and may be reasonably selected according to the actual application requirements.
Alternatively, in order to make the constructed turbine rotor mechanism mathematical model more conform to the aerodynamic characteristics of the turbine rotor, it may be preferable that the turbine rotor mechanism mathematical model constructed according to a mathematical empirical formula is:
；
in the above-mentioned method, the step of,for the mechanism power value, < >>For air density->For the radius of the turbine rotor>For deflection of the fuselageAerodynamic loss of->For the wind speed of the turbine rotor, +.>For the rotational speed of the turbine rotor, +.>For the blade angle of the turbine rotor, +.>、/>、/>、/>、/>、/>Is the undetermined coefficient.
Step S104, training data are obtained by using the turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor.
In this embodiment, the values of the wind speed, blade angle and rotation speed of the turbine rotor are not limited, and can be reasonably selected according to practical application requirements. For example, the value ranges may be the same as those in step S101, the value ranges of the wind speed of the turbine rotor and the value ranges of the wind speed of the turbine rotor in step S101 may be the same, the value ranges of the blade angles of the turbine rotor and the value ranges of the blade angles of the turbine rotor in step S101 may be the same, the value ranges of the rotational speeds of the turbine rotor may float up and down according to the turbine rotor warning rotational speed, and the value ranges of the wind speeds of the turbine rotor may float up and down according to the turbine rotor normal operating condition wind speeds.
Optionally, in order to make the selected training data more accurate, it may be preferable that the range of values of the wind speed of the turbine rotor is:the wind speed can be up and down ten percent according to the normal working condition of the turbine rotor;
the range of the rotating speed of the turbine rotor isTen percent of the rising speed can be reported according to the turbine rotor.
The range of values of the blade angles of the turbine rotor is the same as the range of values of the blade angles of the turbine rotor in step S101.
For example, the wind speed of the turbine rotor may range from [100m/s,165m/s ], the blade angle of the turbine rotor may range from [0 °,90 ° ], and the rotational speed of the turbine rotor may range from [0r/min,7700r/min ].
In this embodiment, after determining the value ranges of the wind speed, the rotation speed and the blade angle of the turbine rotor, selecting a test design method to automatically select points to obtain a plurality of training points representing calculation working conditions, respectively substituting the wind speed value, the rotation speed value and the blade angle value corresponding to each training point into a turbine rotor simulation model to perform simulation operation to obtain corresponding simulation power values, and taking the wind speed value, the rotation speed value, the blade angle value and the simulation power values corresponding to all the calculation working conditions as training data.
Step S105, training to obtain the aerodynamic characteristic high-fidelity model of the turbine rotor of the machine by utilizing a preset machine learning algorithm according to the training data.
In this embodiment, the type of the preset machine learning algorithm is not limited, and a suitable machine learning algorithm can be selected according to practical application requirements, for example, the preset machine learning algorithm can be a neural network algorithm, an intrinsic orthogonal decomposition method, or polynomial fitting.
Alternatively, in order to make the trained model more practical and more generic, the preset machine learning algorithm may be preferably a neural network algorithm.
In the embodiment, a machine learning algorithm is preset to train training data, a data-driven airborne turbine rotor aerodynamic characteristic high-fidelity model is built, and approximate description of a complex mathematical formula of the turbine rotor aerodynamic characteristic is achieved.
Optionally, in order to make the obtained onboard turbine rotor high-fidelity model more computationally efficient and more real-time, and may provide support for the construction of the aerial fueling pod digital twin system, after step S105 is performed, the method may further include:
and S106, obtaining test data by using a turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor.
The test data are used for representing the corresponding relation between the simulation power values and the wind speed value, the blade angle value and the rotating speed value.
Step S107, obtaining a plurality of predicted power values by using the aerodynamic characteristics high-fidelity model of the onboard turbine rotor according to the test data.
Step S108, determining the proportion of the predicted power value which does not meet the preset precision requirement in the plurality of predicted power values according to the root mean square error of the predicted power value and the simulated power value in the test data.
And step S109, when the proportion is smaller than a preset proportion threshold value, correcting the aerodynamic characteristic high-fidelity model of the on-board turbine rotor.
The range of values of the wind speed, the blade angle and the rotation speed of the turbine rotor are the same as the range of values of the wind speed, the blade angle and the rotation speed of the turbine rotor in step S102, and are not described herein.
After the value ranges of the wind speed, the rotating speed and the blade angle of the turbine rotor are determined, the test design method is selected to automatically select points, a plurality of test points representing simulation working conditions are obtained, the wind speed value, the rotating speed value and the blade angle value corresponding to each test point are respectively substituted into a turbine rotor simulation model to be subjected to simulation operation to obtain corresponding simulation power values, and the wind speed value, the rotating speed value, the blade angle value and the simulation power values corresponding to all calculation working conditions are used as test data.
The wind speed value, the rotating speed value and the blade angle value corresponding to each test point are substituted into the airborne turbine rotor aerodynamic characteristic high-fidelity model to be calculated to obtain corresponding predicted power values, and the predicted power values are the predicted power values corresponding to the test points.
The type of the preset precision requirement is not limited, and the preset precision requirement can be set reasonably according to practical application requirements, for example, the root mean square error of the predicted power value and the simulated power value in the test data is smaller than 1, the root mean square error of the predicted power value and the simulated power value in the test data is smaller than 2, and the root mean square error of the predicted power value and the simulated power value in the test data is smaller than 2.1.
The preset ratio threshold is not limited, and can be reasonably selected according to practical application requirements, for example, 90%,80% and 75%.
As can be seen from the above embodiments of the present application, after obtaining a wind speed value, a blade angle value, a rotational speed value and a test power value, the embodiments of the present application solve a turbine rotor mechanism mathematical model by using an optimal solution algorithm to obtain an optimal value of a coefficient to be determined, and combine the optimal value of the coefficient to be determined with the turbine rotor mechanism mathematical model to obtain a turbine rotor simulation model, thereby overcoming the problem of insufficient calculated output power caused by the limitation of test conditions; the turbine rotor simulation model and training data are adopted for training to obtain the aerodynamic characteristic high-fidelity model of the turbine rotor of the machine, the aerodynamic characteristic high-fidelity model of the turbine rotor of the machine is generated by combining actual low-speed test data with a machine algorithm on the basis of a general mechanism mathematical model, different blade configurations of the turbine rotor can be described through a unified model, and the running state parameters of the turbine pump can be comprehensively and accurately monitored through high-fidelity prediction on high-wind speed working conditions.
Example two
As shown in fig. 2, fig. 2 is a schematic flowchart of a modeling method of an airborne turbine rotor aerodynamic feature high-fidelity model disclosed in a second embodiment of the application, the method includes:
step S201, determining wind speed values and rotating speed values corresponding to a plurality of test working condition points by using a first preset sampling point selection method according to the wind speed value range and the rotating speed value range.
In this embodiment, the range of the wind speed is the range of the wind speed of the turbine rotor in step S101 in example one, and the range of the rotational speed is the range of the rotational speed in step S101 in example one, which is not described here again.
In this embodiment, the type of the first preset sampling point selection method is not limited, and may be reasonably selected according to practical application requirements, for example, a latin hypercube point selection method, a full factorial test design method, and an orthogonal test design method may be adopted.
In this embodiment, a plurality of test operating points are determined by adopting a first preset sampling point selection method according to a value range of a wind speed of a turbine rotor and a value range of a rotating speed of the turbine rotor, each test operating point determines a corresponding set of wind speed values and rotating speed values, and the wind speed values and the rotating speed values corresponding to the plurality of test operating points are a plurality of sets of wind speed values and rotating speed values corresponding to the plurality of test operating points.
Step S202, according to the value range of the blade angle, a first value range of the blade angle and a second value range of the blade angle are obtained through sectional design.
In this embodiment, the range of the blade angle is the range of the blade angle of the turbine rotor in step S101 in example one, and will not be described here.
In the embodiment, in order to reduce the number of sampling points in the process of generating the aerodynamic characteristic high-fidelity model of the onboard turbine rotor and enable the points to meet the actual requirements, the value range of the blade angle is designed in a segmented mode. The method for designing the value range of the blade angle in a segmented mode is not limited, reasonable selection can be performed according to practical application requirements, for example, the value range of the blade angle can be equally divided into two sections, the value range of the blade angle can be divided into two sections according to rated working conditions of the blade angle, and the value range of the blade angle can be equally divided into two sections.
Optionally, in order to make the value range of the blade angle after segmentation more scientific, more in line with the actual requirement, preferably,
the first value range of the blade angle is as follows:；
the second value range of the blade angle is as follows:、/>，the blade angle is the blade angle when the turbine reaches the rated working condition.
For example, when the blade angle of the turbine reaches the rated condition and is 60 degrees, the first value range of the blade angle isThe second value range of the blade angle is +.>、/>。
Step S203, according to the first value range of the blade angle and the second value range of the blade angle, the blade angle values corresponding to the test working condition points are determined.
Optionally, in order to reduce the sampling and point selecting times and select more scientifically, and ensure that the test points as few as possible are used to obtain the maximum test information, two ways of random point selecting and equidistant point selecting are adopted to select the blade angle value in the blade angle value range, specifically, step S203 includes:
step 203a, randomly determining first blade angle values corresponding to a plurality of test working condition points by using a second preset sampling point selection method according to the first value range of the blade angle.
The type of the second preset sampling point selection method is not limited, and the second preset sampling point selection method can be reasonably selected according to actual application requirements, for example, the second preset sampling point selection method can be a Latin hypercube test point selection method, a full factorial test design method, and an orthogonal test design method.
Alternatively, to make the randomly selected test operating point more scientific, the second preset sample point selection method may be preferred to be the La Ding Chao cube test point selection method.
According to a first value range of the blade angle of the turbine rotor, a Latin hypercube test design method is adopted to determine a plurality of test operating points, each test operating point determines a corresponding first blade angle value, and the blade angle values corresponding to the test operating points are a plurality of first blade angle values corresponding to the test operating points.
Step 203b, determining second blade angle values corresponding to the plurality of test working condition points at equal intervals by using a preset interval point selection method according to a second value range of the blade angle.
The type of the preset interval point selection method is not limited, and the preset interval point selection method can be reasonably selected according to actual application requirements, for example, a wheel disc algorithm, a simple random sampling algorithm, a hierarchical sampling algorithm and a system sampling algorithm can be adopted.
The selection of the intervals for determining the test working condition points at equal intervals is not limited, and the intervals can be reasonably selected according to actual application requirements, for example, the intervals can be 5 degrees, 10 degrees and 15 degrees.
And determining a plurality of test working condition points at equal intervals by adopting a preset interval point selection method according to a second value range of the blade angle of the turbine rotor, and determining corresponding second blade angle values according to each test working condition point, wherein the blade angle values corresponding to the plurality of test working condition points are a plurality of groups of second blade angle values corresponding to the plurality of test working condition points.
Step S203c, determining blade angle values corresponding to the plurality of test operating points according to the first blade angle values corresponding to the plurality of test operating points and the second blade angle values corresponding to the plurality of test operating points.
The blade angle values corresponding to the test operating points are a set of the first blade angle values obtained in the step S203a and the second blade angle values obtained in the step S203 b.
And S204, performing a turbine rotor aerodynamic characteristic test according to the wind speed value, the blade angle value and the rotating speed value corresponding to each test working point to obtain a test power value corresponding to each test working point.
In this embodiment, compared with step S102 in the first embodiment, the difference between step S204 and step S102 is that the blade angle values corresponding to the test operating points are the aggregate of the first blade angle values obtained in step S203a and the second blade angle values obtained in step S203b, and the other contents are substantially the same or similar, and are not described herein.
Step S205, determining optimal values of a plurality of undetermined coefficients in a pre-built turbine rotor mechanism mathematical model by using a preset optimal solution algorithm according to wind speed values, blade angle values, rotating speed values and test power values corresponding to all test working condition points, and obtaining a turbine rotor simulation model.
In this embodiment, the difference between step S205 and step S103 in the first embodiment is that the blade angle value is a set of the plurality of first blade angle values obtained in step S203a and the plurality of second blade angle values obtained in step S203b, and the other contents are substantially the same or similar, and are not described herein.
Step S206, training data are obtained by using the turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor.
In this embodiment, the content of step S206 is substantially the same as or similar to that of step S104 in the first embodiment, and will not be described herein.
Step S207, training to obtain the aerodynamic characteristic high-fidelity model of the turbine rotor of the machine by utilizing a preset machine learning algorithm according to the training data.
In this embodiment, the content of step S207 is substantially the same as or similar to that of step S105 in the previous embodiment, and will not be described herein.
As can be seen from the above embodiments of the present application, the embodiment of the present application divides the value range of the blade angle into the first value range of the blade angle and the second value range of the blade angle by using the segmentation design, thereby reducing the sampling and point selection times, simultaneously making more scientific point selection, and ensuring that the maximum test information is obtained by using as few test points as possible. And the first value range of the blade angle is randomly selected, the second value range of the blade angle is equally spaced to select points, the sampling point selection times are reduced, the more scientific point selection is realized, and the maximum test information is ensured to be acquired by using as few test points as possible.
Example three
An embodiment of the present application provides a modeling apparatus for an airborne turbine rotor aerodynamic feature hi-fi model, and fig. 3 is a schematic structural diagram of the modeling apparatus for an airborne turbine rotor aerodynamic feature hi-fi model disclosed in the embodiment of the present application, the apparatus includes:
the information value determining module 301 is configured to determine a wind speed value, a blade angle value, and a rotation speed value corresponding to a plurality of test operating points according to a range of values of a wind speed, a blade angle, and a rotation speed of the turbine rotor;
the test power value determining module 302 is configured to perform a turbine rotor aerodynamic characteristic test according to the wind speed value, the blade angle value, and the rotational speed value corresponding to each test operating point to obtain a test power value corresponding to each test operating point;
the comprehensive obtaining module 303 is configured to determine optimal values of a plurality of undetermined coefficients in a pre-constructed turbine rotor mechanism mathematical model by using a preset optimal solution algorithm according to wind speed values, blade angle values, rotation speed values and test power values corresponding to all test operating points, and obtain a turbine rotor simulation model; the turbine rotor mechanism mathematical model is used for calculating and obtaining a mechanism power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the turbine rotor simulation model is used for calculating and obtaining a simulation power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the optimization target of the optimal solution algorithm is that the root mean square error of the test power value and the simulation power value is minimum, and the constraint condition of the preset optimal solution algorithm is the value range of a plurality of undetermined coefficients;
The data obtaining module 304 is configured to obtain training data by using a turbine rotor simulation model according to a value range of a wind speed, a blade angle and a rotation speed of the turbine rotor; the training data are used for representing the corresponding relation between the simulation power values and the wind speed value, the blade angle value and the rotating speed value;
the high-fidelity model obtaining module 305 is configured to obtain a high-fidelity model of aerodynamic characteristics of the turbine rotor on board by training through a preset machine learning algorithm according to the training data; the airborne turbine rotor aerodynamic characteristic high-fidelity model is used for calculating and obtaining a predicted power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor.
Optionally, the device is further used for obtaining test data by using a turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor; the test data are used for representing the corresponding relation between the simulation power values and the wind speed value, the blade angle value and the rotating speed value; according to the test data, a plurality of predicted power values are obtained by using an airborne turbine rotor aerodynamic characteristic high-fidelity model; determining the proportion of the predicted power value which does not meet the preset precision requirement in the plurality of predicted power values according to the root mean square error of the predicted power value and the simulation power value in the test data; and when the ratio is smaller than a preset ratio threshold value, correcting the aerodynamic characteristic high-fidelity model of the on-board turbine rotor.
Optionally, the information value determining module 301 is further configured to determine, according to the value range of the wind speed and the value range of the rotation speed, a wind speed value and a rotation speed value corresponding to a plurality of test operating points by using a first preset sampling point selection method; according to the value range of the blade angle, carrying out sectional design to obtain a first value range of the blade angle and a second value range of the blade angle; wherein, the aggregate of the first value range of the blade angle and the second value range of the blade angle is the value range of the blade angle; and determining blade angle values corresponding to the test working condition points according to the first value range of the blade angle and the second value range of the blade angle.
Further, the information value determining module 301 is further configured to randomly determine, according to a first value range of blade angles, first blade angle values corresponding to a plurality of test operating points by using a second preset sampling point selection method; determining second blade angle values corresponding to a plurality of test working condition points at equal intervals by using a preset interval point selection method according to a second value range of the blade angle; and determining the blade angle values corresponding to the test working points according to the first blade angle values corresponding to the test working points and the second blade angle values corresponding to the test working points.
Alternatively, the turbine rotor mechanism mathematical model constructed in the synthesis acquisition module 303 is:
；
in the above-mentioned method, the step of,for principle power value>For air density->For the radius of the turbine rotor>Aerodynamic losses for fuselage deflection, +.>For the wind speed of the turbine rotor>For the rotational speed of the turbine rotor>For the blade angle of the turbine rotor->、/>、/>、/>、/>、/>Is a coefficient to be determined.
As can be seen from the above embodiments of the present application, by using the modeling apparatus for the aerodynamic characteristics of the airborne turbine rotor according to the present embodiment, the modeling method for the aerodynamic characteristics of the airborne turbine rotor according to the foregoing multiple method embodiments may be implemented, and the beneficial effects of the corresponding method embodiments are not described herein.
Thus, specific embodiments of the present application have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be apparent to those skilled in the art that embodiments of the present application may be provided as methods or apparatus. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (9)
1. A method for modeling an aerodynamic feature high-fidelity model of an on-board turbine rotor, the method comprising:
according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor, determining wind speed values, blade angle values and rotating speed values corresponding to a plurality of test working condition points;
according to the wind speed value, the blade angle value and the rotating speed value corresponding to each test working point, performing a turbine rotor aerodynamic characteristic test to obtain a test power value corresponding to each test working point;
according to the wind speed value, the blade angle value, the rotating speed value and the test power value corresponding to all the test working condition points, determining the optimal values of a plurality of undetermined coefficients in a pre-constructed turbine rotor mechanism mathematical model by using a preset optimal solution algorithm, and obtaining a turbine rotor simulation model; the turbine rotor mechanism mathematical model is used for calculating and obtaining a mechanism power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the turbine rotor simulation model is used for calculating and obtaining a simulation power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the optimization target of the optimal solution algorithm is that the root mean square error of the test power value and the simulation power value is minimum; the constraint condition of the preset optimal solution algorithm is the value range of a plurality of undetermined coefficients;
Obtaining training data by using the turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor; the training data are used for representing the corresponding relation between a plurality of simulation power values and wind speed values, blade angle values and rotating speed values;
according to the training data, training by using a preset machine learning algorithm to obtain a high-fidelity model of aerodynamic characteristics of the turbine rotor on board; the airborne turbine rotor aerodynamic characteristic high-fidelity model is used for calculating and obtaining a predicted power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor;
wherein, turbine rotor mechanism mathematical model is:
；
in the above-mentioned method, the step of,for the mechanism power value, < >>For air density->For the radius of the turbine rotor>Aerodynamic losses for fuselage deflection, +.>For the wind speed of the turbine rotor, +.>For the rotational speed of the turbine rotor, +.>For the blade angle of the turbine rotor, +.>、/>、/>、/>、/>、/>Is the undetermined coefficient.
2. The method of claim 1, wherein determining wind speed values, blade angle values, and rotational speed values corresponding to a plurality of test operating points based on the range of values for wind speed, blade angle, and rotational speed of the turbine rotor comprises:
Determining wind speed values and rotating speed values corresponding to a plurality of test working condition points by using a first preset sampling point selection method according to the wind speed value range and the rotating speed value range;
according to the value range of the blade angle, carrying out sectional design to obtain a first value range of the blade angle and a second value range of the blade angle; wherein, the first value range of the blade angle and the second value range of the blade angle are combined to form the value range of the blade angle;
and determining blade angle values corresponding to a plurality of test working condition points according to the first value range of the blade angle and the second value range of the blade angle.
3. The method of claim 2, wherein determining blade angle values for a plurality of test operating points based on the first range of blade angles and the second range of blade angles comprises:
according to the first value range of the blade angle, a second preset sampling point selection method is utilized to randomly determine first blade angle values corresponding to a plurality of test working condition points;
determining second blade angle values corresponding to a plurality of test working condition points at equal intervals by using a preset interval point selection method according to the second value range of the blade angle;
And determining the blade angle values corresponding to the test working condition points according to the first blade angle values corresponding to the test working condition points and the second blade angle values corresponding to the test working condition points.
4. A method as claimed in claim 3, wherein the second predetermined sampling point selection method is a pull Ding Chao cubic test point selection method.
5. The method of claim 1, wherein the predetermined optimal solution algorithm is a particle swarm algorithm.
6. The method of claim 1, wherein,the value range of (2) is +.>，/>The range of the values is as follows，/>The value range of (2) is +.>，/>The value range of (2) is +.>，/>The value range of (2) is +.>，/>The value range of (2) is +.>。
7. The method of claim 1, wherein the pre-set machine learning algorithm is a neural network algorithm.
8. The method of claim 1, wherein after training to obtain the on-board turbine rotor aerodynamic feature high-fidelity model using a preset machine learning algorithm based on the training data, the method further comprises:
obtaining test data by using the turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor; the test data are used for representing the corresponding relation between a plurality of simulation power values and wind speed values, blade angle values and rotating speed values;
Obtaining a plurality of predicted power values by using the aerodynamic characteristic high-fidelity model of the onboard turbine rotor according to the test data;
determining the proportion of the predicted power value which does not meet the preset precision requirement in the plurality of predicted power values according to the root mean square error of the predicted power values and the simulation power values in the test data;
and when the ratio is smaller than a preset ratio threshold value, correcting the aerodynamic characteristic high-fidelity model of the airborne turbine rotor.
9. An on-board turbine rotor aerodynamic feature high-fidelity model modeling apparatus, the apparatus comprising:
the information value determining module is used for determining wind speed values, blade angle values and rotating speed values corresponding to a plurality of test working condition points according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor;
the test power value determining module is used for carrying out a turbine rotor aerodynamic characteristic test according to the wind speed value, the blade angle value and the rotating speed value corresponding to each test working point to obtain a test power value corresponding to each test working point;
the comprehensive obtaining module is used for determining optimal values of a plurality of undetermined coefficients in a pre-built turbine rotor mechanism mathematical model by utilizing a preset optimal solution algorithm according to wind speed values, blade angle values, rotating speed values and test power values corresponding to all test working condition points, and obtaining a turbine rotor simulation model; the turbine rotor mechanism mathematical model is used for calculating and obtaining a mechanism power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the turbine rotor simulation model is used for calculating and obtaining a simulation power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor; the optimization target of the optimal solution algorithm is that the root mean square error of the test power value and the simulation power value is minimum, and the constraint condition of the preset optimal solution algorithm is the value range of a plurality of undetermined coefficients;
The data acquisition module is used for acquiring training data by utilizing the turbine rotor simulation model according to the value ranges of the wind speed, the blade angle and the rotating speed of the turbine rotor; the training data are used for representing the corresponding relation between a plurality of simulation power values and wind speed values, blade angle values and rotating speed values;
the high-fidelity model obtaining module is used for obtaining the aerodynamic characteristic high-fidelity model of the turbine rotor of the machine by training through a preset machine learning algorithm according to the training data; the airborne turbine rotor aerodynamic characteristic high-fidelity model is used for calculating and obtaining a predicted power value according to the wind speed, the blade angle and the rotating speed of the turbine rotor;
wherein, turbine rotor mechanism mathematical model is:
；
in the above-mentioned method, the step of,for the mechanism power value, < >>For air density->For the radius of the turbine rotor>Aerodynamic losses for fuselage deflection, +.>For the wind speed of the turbine rotor, +.>For the rotational speed of the turbine rotor, +.>For the blade angle of the turbine rotor, +.>、/>、/>、/>、/>、/>Is the undetermined coefficient.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102725520A (en) * | 2009-12-21 | 2012-10-10 | 维斯塔斯风力系统集团公司 | A wind turbine having a control method and controller for performing predictive control of a wind turbine generator |
| CN104454350A (en) * | 2013-09-23 | 2015-03-25 | 通用电气公司 | Wind turbine and control method for lowering unbalanced load of rotor of wind turbine |
| WO2019165753A1 (en) * | 2018-02-28 | 2019-09-06 | 北京金风科创风电设备有限公司 | Load prediction method and apparatus for wind power generator set |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102725520A (en) * | 2009-12-21 | 2012-10-10 | 维斯塔斯风力系统集团公司 | A wind turbine having a control method and controller for performing predictive control of a wind turbine generator |
| CN104454350A (en) * | 2013-09-23 | 2015-03-25 | 通用电气公司 | Wind turbine and control method for lowering unbalanced load of rotor of wind turbine |
| WO2019165753A1 (en) * | 2018-02-28 | 2019-09-06 | 北京金风科创风电设备有限公司 | Load prediction method and apparatus for wind power generator set |
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